Multiobjective Optimization Model for Wind Power Allocation

Autores
Alemany, Juan Manuel; Magnago, Fernando; Lombardi, Pio; Arendarski, Bartlomiej; Komarnicki, Przemyslaw
Año de publicación
2017
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented -constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.
Fil: Alemany, Juan Manuel. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Magnago, Fernando. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lombardi, Pio. IFF Fraunhofer; Alemania
Fil: Arendarski, Bartlomiej. IFF Fraunhofer; Alemania
Fil: Komarnicki, Przemyslaw. IFF Fraunhofer; Alemania
Materia
ENERGIA EOLICA
OPTIMIZACION MULTIOBJETIVO
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/72367
Acceder
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network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Multiobjective Optimization Model for Wind Power AllocationAlemany, Juan ManuelMagnago, FernandoLombardi, PioArendarski, BartlomiejKomarnicki, PrzemyslawENERGIA EOLICAOPTIMIZACION MULTIOBJETIVOhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented -constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.Fil: Alemany, Juan Manuel. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Magnago, Fernando. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lombardi, Pio. IFF Fraunhofer; AlemaniaFil: Arendarski, Bartlomiej. IFF Fraunhofer; AlemaniaFil: Komarnicki, Przemyslaw. IFF Fraunhofer; AlemaniaHindawi Publishing Corporation2017-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/72367Alemany, Juan Manuel; Magnago, Fernando; Lombardi, Pio; Arendarski, Bartlomiej; Komarnicki, Przemyslaw; Multiobjective Optimization Model for Wind Power Allocation; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2017; 3-2017; 1-10; 18769341024-123X1563-5147CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1155/2017/1876934info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/mpe/2017/1876934/info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:32:34Zoai:ri.conicet.gov.ar:11336/72367instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:32:34.357CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Multiobjective Optimization Model for Wind Power Allocation
title Multiobjective Optimization Model for Wind Power Allocation
spellingShingle Multiobjective Optimization Model for Wind Power Allocation
Alemany, Juan Manuel
ENERGIA EOLICA
OPTIMIZACION MULTIOBJETIVO
title_short Multiobjective Optimization Model for Wind Power Allocation
title_full Multiobjective Optimization Model for Wind Power Allocation
title_fullStr Multiobjective Optimization Model for Wind Power Allocation
title_full_unstemmed Multiobjective Optimization Model for Wind Power Allocation
title_sort Multiobjective Optimization Model for Wind Power Allocation
dc.creator.none.fl_str_mv Alemany, Juan Manuel
Magnago, Fernando
Lombardi, Pio
Arendarski, Bartlomiej
Komarnicki, Przemyslaw
author Alemany, Juan Manuel
author_facet Alemany, Juan Manuel
Magnago, Fernando
Lombardi, Pio
Arendarski, Bartlomiej
Komarnicki, Przemyslaw
author_role author
author2 Magnago, Fernando
Lombardi, Pio
Arendarski, Bartlomiej
Komarnicki, Przemyslaw
author2_role author
author
author
author
dc.subject.none.fl_str_mv ENERGIA EOLICA
OPTIMIZACION MULTIOBJETIVO
topic ENERGIA EOLICA
OPTIMIZACION MULTIOBJETIVO
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented -constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.
Fil: Alemany, Juan Manuel. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Magnago, Fernando. Universidad Nacional de Río Cuarto; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lombardi, Pio. IFF Fraunhofer; Alemania
Fil: Arendarski, Bartlomiej. IFF Fraunhofer; Alemania
Fil: Komarnicki, Przemyslaw. IFF Fraunhofer; Alemania
description There is an increasing need for the injection to the grid of renewable energy; therefore, to evaluate the optimal location of new renewable generation is an important task. The primary purpose of this work is to develop a multiobjective optimization model that permits finding multiple trade-off solutions for the location of new wind power resources. It is based on the augmented -constrained methodology. Two competitive objectives are considered: maximization of preexisting energy injection and maximization of new wind energy injection, both embedded, in the maximization of load supply. The results show that the location of new renewable generation units affects considerably the transmission network flows, the load supply, and the preexisting energy injection. Moreover, there are diverse opportunities to benefit the preexisting generation, contrarily to the expected effect where renewable generation displaces conventional power. The proposed methodology produces a diverse range of equivalent solutions, expanding and enriching the horizon of options and giving flexibility to the decision-making process.
publishDate 2017
dc.date.none.fl_str_mv 2017-03
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/72367
Alemany, Juan Manuel; Magnago, Fernando; Lombardi, Pio; Arendarski, Bartlomiej; Komarnicki, Przemyslaw; Multiobjective Optimization Model for Wind Power Allocation; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2017; 3-2017; 1-10; 1876934
1024-123X
1563-5147
CONICET Digital
CONICET
url http://hdl.handle.net/11336/72367
identifier_str_mv Alemany, Juan Manuel; Magnago, Fernando; Lombardi, Pio; Arendarski, Bartlomiej; Komarnicki, Przemyslaw; Multiobjective Optimization Model for Wind Power Allocation; Hindawi Publishing Corporation; Mathematical Problems in Engineering; 2017; 3-2017; 1-10; 1876934
1024-123X
1563-5147
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1155/2017/1876934
info:eu-repo/semantics/altIdentifier/url/https://www.hindawi.com/journals/mpe/2017/1876934/
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Hindawi Publishing Corporation
publisher.none.fl_str_mv Hindawi Publishing Corporation
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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